Adding functions to produce impact, coverage and fvp plots

.github/workflows/R-CMD-check.yaml

@@ -4,7 +4,7 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
+    branches: [*]
 
 name: R-CMD-check
 

.github/workflows/lint-changed-files.yaml

@@ -3,7 +3,7 @@
 on:
   workflow_dispatch:
   pull_request:
-    branches: [main, master]
+    branches: [*]
     paths:
       - '**.R'
       - '**.Rmd'

R/constants.R

@@ -215,3 +215,11 @@ DEF_TOUCHSTONE_OLD_OLD <- "202110"
 #'
 #' @export
 COLOUR_VIMC <- "#008080"
+
+#' @name constants
+#' 
+#' @examples
+#' pine
+#' 
+#' @export
+pine <- c("PAK", "IND", "NGA", "ETH")

R/fn_plotting_impact.R

@@ -0,0 +1,274 @@
+#' Plot central impact estimates by cohort and year. 
+#' 
+#' Produces faceted plots of central impact estimates for priority countries,
+#' stratified either by birth cohort or by year of vaccination.
+#' Impact metrics include cases, deaths, DALYs, and YLLs.
+#' 
+#' @param data A tibble containing impact estimates.
+#' @param burden_type Burden metric used to evaluate impact. burden_type can be: cases, deaths, dalys, yll.
+#' @param title Title of the plot to be rendered
+#' @param view Charactar scalar. The way impact is assigned, either by birth cohort ("cohort")  or by year of vaccination ("year").
+#'  
+#' @return ggplot object showing central impact estimates 
+#'  
+#' @examples
+#' 
+#' # Create example data
+#' impact_data <- tibble::tibble(
+#'   country = c("A", "A", "B", "B"),
+#'   year = c(2020, 2021, 2020, 2021),
+#'   birth_cohort = c(2000, 2001, 2000, 2001),
+#'   burden_outcome = c("deaths", "cases", "deaths", "cases"),
+#'   impact = c(15, 5, 14, 8),
+#'   short_name = c("short1", "short2", "short3", "short4")
+#' )
+#' plot_impact(
+#'   data = impact_data,
+#'   burden_type = "cases",
+#'   title = "Cases averted",
+#'   view = "year"
+#' )
+#'  
+#' @export
+plot_impact <- function(
+    data, 
+    burden_type = c("cases", "deaths", "dalys", "yll"),
+    title, 
+    view = c("cohort", "year")
+){
+  checkmate::assert_tibble(data, min.rows = 1L, min.cols = 1L)
+
+  required_cols <- c("country", "burden_outcome", "impact", "short_name")
+
+  checkmate::assert_names(
+    names(data),
+    must.include = required_cols
+  )
+
+  checkmate::assert_character(title, len = 1)
+
+  burden_type <- rlang::arg_match(burden_type)
+  view <- rlang::arg_match(view)
+
+  Impact <- dplyr::filter(data, 
+                          .data$country %in% pine,
+                          .data$burden_outcome == burden_type,
+                          .data$impact != 0)
+
+  if(nrow(Impact) > 0){
+# ---- Cohort view ----
+    if(view == "cohort"){
+
+      checkmate::assert_names(names(data), must.include = "birth_cohort")
+
+      Impact <- Impact %>% dplyr::rename(Impact, cohort = .data$birth_cohort)
+
+      cols_to_select <- c("country", "cohort", "impact", "short_name")
+
+      Impact <- dplyr::select(Impact, all_of(cols_to_select))
+
+      p <- ggplot(
+        Impact,
+        aes(
+          x = .data$cohort, 
+          y = .data$impact, 
+          ymin = .data$impact, 
+          ymax = .data$impact, 
+          fill = as.character(.data$short_name)
+          ) 
+        ) +
+        ggplot::geom_ribbon(alpha = 0.3) +
+        ggplot::geom_line(aes(colour = .data$short_name), size = 0.5)+
+        ggplot::geom_point(aes(colour = .data$short_name), size = 0.5)+
+        theme_vimc() + #TODO: to check where the theme definition is saved as may not be right for this plot
+        facet_wrap(country~., scales = "free_y") +
+        labs(
+          x = "Birth cohort",
+          y = paste(burden_type, "averted"),
+          title = title
+          ) +
+        theme(
+          legend.position="bottom", 
+          legend.key.size= unit(0.5, 'cm'),
+          legend.key.width = unit(0.3, 'cm')
+        )
+
+    } else { # ---- Year (non-cohort) view ----
+      cols_to_select <- c("country", "year", "impact", "short_name")
+
+      checkmate::assert_names(names(data), must.include = "year")
+
+      Impact <- dplyr::select(Impact, all_of(cols_to_select))
+
+      p <- ggplot (
+        Impact,
+        aes(
+          x = .data$year, 
+          y = .data$impact, 
+          ymin = .data$impact, 
+          ymax = .data$impact, 
+          fill = .data$short_name
+          )
+        ) +
+        ggplot::geom_ribbon(alpha = 0.3)+
+        ggplot::geom_line(aes(colour = .data$short_name), size = 0.5)+
+        ggplot::geom_point(aes(colour = .data$short_name), size = 0.5)+
+        theme_vimc() + #TODO: same note as above re theme definition
+        facet_wrap(country~., scales = "free_y")+
+        labs(
+          x = "Year", 
+          y = paste(burden_type, "averted"),
+          title = title
+          ) +
+        theme(
+          legend.position="bottom", 
+          legend.key.size= unit(0.5, 'cm'),
+          legend.key.width = unit(0.3, 'cm')
+        )
+    }
+  } else {
+    p <- "No estimates in the data." #TODO: both here and in the below plot returning p may be an issue? Can you think of a better way?
+  }
+  return(p)
+
+}
+
+#' Plot coverage and fully vaccinated persons (FVPs)
+#' 
+#' Generates plots of routine vaccine coverage and fully vaccinated
+#' persons (FVPs) over time for selected countries.
+#' 
+#' @param fvps A tibble showing the number of fvps (fully vaccinated persons) 
+#'  by country, year and scenario/activity type.
+#'  
+#' @return A named list with two ggplot objects:
+#'   \describe{
+#'     \item{coverage}{A plot of routine vaccine coverage over time.}
+#'     \item{fvps}{A plot of fully vaccinated persons over time.}
+#'   }
+#' @examples
+#' 
+#' # Create example data
+#' fvps <- tibble::tibble(
+  #'   country = c("AGO", "AGO", "BEN", "BEN"),
+  #'   year = c(2020, 2021, 2020, 2021),
+  #'   activity_type = c("routine", "campaign", "routine", "campaign"),
+  #'   scenario_type = c("default", "default", "default", "default"),
+  #'   vaccine = c("measles", "measles", "measles", "measles"),
+  #'   coverage_adjusted = c(0.8, 0.85, 0.4, 0.7),
+  #'   fvps = c(1000000, 1200000, 800000, 900000)
+  #' )
+
+#' plots <- plot_coverage_fvps(fvps)
+#' plots$coverage
+#' plots$fvps
+#' 
+#' @export 
+plot_coverage_fvps <- function(fvps){
+  checkmate::assert_tibble(fvps, min.rows = 1L, min.cols = 1L)
+
+  required_cols <- c(
+    "country",
+    "activity_type",
+    "scenario_type",
+    "vaccine",
+    "coverage_adjusted",
+    "year",
+    "fvps"
+  )
+
+  checkmate::assert_names(
+    names(fvps),
+    must.include = required_cols
+  )
+
+
+  fvps <- dplyr::filter(fvps, .data$country %in% pine)
+
+  cov <- dplyr::filter(fvps, .data$activity_type == "routine")
+
+  cov <- dplyr::mutate(cov,
+      vaccine_delivery = paste(.data$scenario_type, .data$vaccine, sep = "_"),
+      coverage_adjusted = round(.data$coverage_adjusted*100, 2)
+      )
+
+  cols_to_select <- c("country", "vaccine_delivery", "year", "coverage_adjusted")
+
+  cov <- dplyr::select(cov, all_of(cols_to_select))
+
+  cov <- dplyr::rename(cov, coverage = .data$coverage_adjusted) 
+
+  fvps <-  dplyr::mutate(fvps,
+                        vaccine_delivery = paste(.data$scenario_type, .data$activity_type, sep = "_")
+                        )
+  cols_to_select <- c("country", "vaccine_delivery", "year", "fvps")
+
+  fvps <- dplyr::select(fvps, all_of(cols_to_select)) 
+
+  fvps <- dplyr::group_by(fvps,
+                          .data$country, 
+                          .data$vaccine_delivery, 
+                          .data$year) 
+
+  fvps <- dplyr::summarise(fvps, 
+                           fvps = round(sum(.data$fvps)/1e6, 2),
+                           .groups = "drop"
+                           ) 
+  if(nrow(cov) > 0){
+    p <- ggplot(
+      cov, 
+      aes(
+        x = .data$year, 
+        y = .data$coverage, 
+        ymin = 0, 
+        ymax = 1, 
+        fill = .data$vaccine_delivery) 
+      ) +
+      ggplot::geom_line(aes(colour = .data$vaccine_delivery), size = 0.5) +
+      theme_vimc() + #TODO: same note as above
+      facet_wrap(country~., scales = "free_y")+
+      labs(
+        x = "Year", 
+        y = "Coverage (%)",
+        title = "Routine vaccine coverage" 
+        ) +
+      theme(
+        legend.position="bottom", 
+        legend.key.size= unit(0.5, 'cm'),
+        legend.key.width = unit(0.3, 'cm')
+) 
+
+  } else {
+    p <- "There is no routine coverage in the database."
+  }
+
+
+  q <- ggplot(
+    fvps,
+    aes(
+      x = .data$year, 
+      y = .data$fvps, 
+      ymin = .data$fvps, 
+      ymax = .data$fvps, #TODO: min/max both here and above seem to be the same so may be irrelevant to define
+      fill = .data$vaccine_delivery
+    )
+      ) +
+    geom_point(aes(colour = .data$vaccine_delivery), size = 0.5) +
+    theme_vimc()+ #TODO: same note above on theme 
+    facet_wrap(country~., scales = "free_y") +
+    labs(
+      x = "Year", 
+      y = "FVPs (in millions)",
+      title = "FVPs"
+      ) +
+    theme(
+      legend.position="bottom", 
+      legend.key.size = unit(0.5, 'cm'),
+      legend.key.width = unit(0.3, 'cm')
+      )
+
+  return(list(
+    coverage = p,
+    fvps = q
+    ))
+}

_pkgdown.yml

@@ -32,6 +32,12 @@ reference:
   - subtitle: Plot impact estimates
     contents:
     - plot_impact_diagnostics
+  - subtitle: Plot central impact estimates by cohort and year
+    contents: 
+    - plot_impact
+  - subtitle: Plot coverage and fully vaccinated persons (FVPs)
+    contents:
+    - plot_coverage_fvps
 
   - title: Plotting helper functions
     contents: